Budget Constrained Interactive Search for Multiple Targets

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Abstract

Interactive graph search leverages human intelligence to categorize target labels in a hierarchy, which are useful for image classification, product categorization, and database search. However, many existing studies of interactive graph search aim at identifying a single target optimally, and suffer from the limitations of asking too many questions and not being able to handle multiple targets. To address these two limitations, in this paper, we study a new problem of Budget constrained Interactive Graph Search for Multiple targets called lBM-IGS-problem. Specifically, given a set of multiple targets T in a hierarchy, and two parameters k and b, the goal is to identify a k-sized set of selections S such that the closeness between selections S and targets T is as small as possible, by asking at most a budget of b questions. We theoretically analyze the updating rules and design a penalty function to capture the closeness between selections and targets. To tackle the kBM-IGS-problem, we develop a novel framework to ask questions using the best vertex with the largest expected gain, which makes a balanced trade-off between target probability and benefit gain. Based on the kBM-IGS framework, we then propose an efficient algorithm STBIS to tackle a special case of the SingleTarget problem. Furthermore, we propose a dynamic programming based method kBM-DP to tackle kBM-IGS. To further improve efficiency, we propose two approximate methods kBM-Topk and kBM-DP+. Extensive experiments on large real-world datasets with ground-truth targets verify both the effectiveness and efficiency of our proposed algorithms.